advertisement

2D SOM Bezier Spline

This sketch represents a 2D self organising feature map. The SOM is presented with a number of bezier splines to cognize/learn. These inputs can be seen along the bottom. The grey circles represent the learn radius.

Embed Code

A SOM does not need a target output to be specified unlike many other types of network. Instead, where the node weights match the input vector, that area of the lattice is selectively optimized to more closely resemble the data for the class the input vector is a member of. From an initial distribution of random weights, and over many iterations, the SOM eventually converges into a map of stable zones. Each zone is effectively a feature classifier, the graphical output can be thought of as a type of feature map of the input space.